CN103993108A - Liquid level measurement method of suction well in blast furnace slag treatment system - Google Patents

Abstract

The invention provides a liquid level measurement method of a suction well in a blast furnace slag treatment system. The method comprises the following steps: building a fuzzy inference model of the quantity of the liquid level change of the suction well caused by the volume flow of a water replenishment valve, and calculating the quantity of the liquid level change of the suction well caused by the volume flow of the water replenishment valve; building a fuzzy inference model of the quantity of the liquid level change of the suction well caused by the lost volume flow of a slag treatment water circulation system, and calculating the quantity of the liquid level change of the suction well caused by the lost volume flow of a slag treatment water circulation system; obtaining a calculation formula of the total quantity of the liquid level change of the suction well. The method is suitable for complicated severe working conditions of blast furnace ironmaking; an accurate suction well liquid level signal can be provided for a granulation water supply pump control system when a liquid level meter has a fault, so that stable operation of the blast furnace slag treatment system is ensured and the production automation level of blast furnace ironmaking at present is increased.

Description

A kind of blast furnace slag processing system suction well level measuring method

Technical field

The invention belongs to blast furnace ironmaking field, relate in particular to a kind of blast furnace slag processing system suction well level measuring method.

Background technology

In metallurgy industry, blast furnace iron-making process process accounts for 70% of iron and steel enterprise's total energy consumption, is that big power consumer and the efficiency of energy utilization of iron and steel enterprise is low, therefore having a high potential of its energy-saving and emission-reduction.As everyone knows, the steady direct motion of blast furnace is the key link in blast furnace ironmaking production process, and in the slag processing links large system that is blast furnace, has the key link of energy-saving and emission-reduction potentiality.Slag disposal system is divided into according to dewatering type: 1) settling tank method; 2) rotary drum evaporation; 3) slag bath filtration method; 4) Ming Tefa.Wherein, Ming Tefa has facility compact, take up an area little, the failure rate is low in space, pollution less, cost of investment is low, the high temperature sludge and the bulk slag that in particular cases produce are had to the advantages such as good processing power, thereby are extensively adopted by newly-built blast furnace slag processing system.Slag, after molten iron separates, enters granulation district through slag ditch, ejects current slag granulating is cooling by Punched box.

In granulation working shaft Controlling System, the interlocking condition of working shaft starting is that suction well liquid level must be greater than certain numerical value, now can start working shaft, and this has also been avoided water pump draws air, has then protected water pump.In this case, extremely important to the accurate detection of suction well liquid level.In blast furnace slag processing system situ production, operating mode is very severe often, and exposed very high in outdoor liquidometer spoilage, this reliability to Controlling System has produced disadvantageous effect.Therefore,, in the time of liquidometer fault, in order to ensure the normal work of granulation working shaft Controlling System, just need to set up a kind of effective blast furnace slag processing system suction well liquid level flexible measurement method and substitute the measuring ability of liquidometer.

Document " the new No. 2 blast furnace Ming Tefa slags of the large special steel in side are processed automation system research and optimize ", metallurgical automation, Vol.37, No.1, in 2013, author has proposed a kind of suction well liquid level mathematical model and has replaced liquidometer measurement.For the complicacy of simplifying model, in paper, author has provided two hypothesis: the variation of 1) supposing slag processing water cycle volumetric flow rate variable quantity and altitude loss is approximated to direct ratio and is inversely proportional to liquid resistance; 2) before and after water compensating valve, pressure reduction is constant.In actual production process, these two assumed conditions are difficult to be met, and that is to say, the measuring method that above-mentioned document provides can not meet on-the-spot needs.Therefore, research and development are applicable to the suction well level measuring method under complicated bad working environments, for granulation working shaft Controlling System provides suction well liquid level signal accurately, are key technical problems urgently to be resolved hurrily that further improves current blast furnace ironmaking production automation level.

Summary of the invention

The technical problem to be solved in the present invention is: a kind of blast furnace slag processing system suction well level measuring method is provided; the high precision suction well liquid level adapting under blast furnace ironmaking production complex working conditions detects online; for ensureing that the smooth running of granulation working shaft Controlling System provides reliable judgment basis; thereby produce high-quality molten iron product, improve current blast furnace ironmaking production automation level.

The present invention for solving the problems of the technologies described above taked technical scheme is: a kind of blast furnace slag processing system suction well level measuring method, is characterized in that: it comprises the following steps:

1) set up water compensating valve volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF}(P_1-P_2)\mathrm{isS}1,\mathrm{THEN\Δ}{h}_1=k_1\×\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isM}1,\mathrm{THEN\Δ}{h}_1=k_2\×\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isB}1,\mathrm{THEN\Δ}{h}_1=k_3\×\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

Wherein P ₁for the pressure before water compensating valve, P ₂for the pressure after water compensating valve; S1, M1, B1 are respectively description (P ₁– P ₂) be little, in, large fuzzy number; Δ h ₁for water compensating valve volumetric flow rate causes suction well liquid level change amount; k _jfor the gain factor under j article of fuzzy rule, be dimensionless unit, j=1,2,3, k _jinitial value by technologist by obtaining in manual operation experimental knowledge; A is orifice coefficient, and it depends on structural shape and the fluid flow of water compensating valve; A ₀for water compensating valve is taken over sectional area; T is the PLC systematic sampling cycle; G is universal gravity constant; R is fluid severe; A is suction well sectional area;

2) in conjunction with (P ₁– P ₂) size the influencing characteristic of suction well liquid level change amount is set about (P ₁– P ₂) fuzzy membership functions:

(P ₁– P ₂) about the fuzzy membership functions f of S1 _s(P ₁– P ₂):

$f_s(P_1-P_2)=\left\{\begin{array}{cc}1,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_2\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of M1 _m(P ₁– P ₂):

$f_M(P_1-P_2)=\left\{\begin{array}{cc}0,& \text{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_3}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_2\&le;(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of B1 _b(P ₁– P ₂):

$f_B(P_1-P_2)=\left\{\begin{array}{cc}0,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_3}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3},& \mathrm{if}{\mathrm{\&alpha;}}_2<(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 1,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.;$

Here α, ₁for (P ₁– P ₂) be little threshold value, α ₂for (P ₁– P ₂) threshold value in being, α ₃for (P ₁– P ₂) be large threshold value; In Fuzzy Inference Model, think (P ₁– P ₂) be less than α ₁time be S1, (P ₁– P ₂) equal α ₂time be M1, (P ₁– P ₂) be greater than α ₃time be B1;

3) utilize water compensating valve volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about (P ₁– P ₂) fuzzy membership functions, set up following water compensating valve volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\begin{array}{c}\mathrm{\&Delta;}{h}_1=f_s(P_1-P_2)\&times;k_1\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}+f_M(P_1-P_2)\&times;k_2\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}\\ +f_B(P_1-P_2)\&times;k_3\&times;\frac{a{A}_{0}t\sqrt{\left(P_1{-P}_2\right)\frac{2g}{r}}}{A}\end{array},$

4) set up slag and process water circulation system loss volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_1\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_2\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_3=m_3\&times;e^{\frac{t}{R_{2}A}};$

Wherein Δ Q ₂for slag cycle for the treatment of system loss volumetric flow rate; S2, M2, B2 are respectively and describe Δ Q ₂for little, in, large fuzzy number; Δ h ₂for slag cycle for the treatment of system loss volumetric flow rate causes suction well liquid level change amount; m _ifor the gain factor under i article of fuzzy rule, be dimensionless unit, m _iinitial value by technologist by obtaining in manual operation experimental knowledge; R ₂for the liquid resistance of flushing cinder water, by measuring;

5) in conjunction with Δ Q ₂size the influencing characteristic of suction well liquid level change amount is set about Δ Q ₂fuzzy membership functions:

Δ Q ₂fuzzy membership functions about S2:

$f_s\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}1,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_2\end{array}\right.,$

Δ Q ₂fuzzy membership functions about M2:

$f_M\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_3}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_2\&le;\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array},,\right.$

Δ Q ₂fuzzy membership functions about B2:

$f_B\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3},& \mathrm{if}{\mathrm{\&beta;}}_2<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 1,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array}\right.;$

β ₁for Δ Q ₂for little threshold value, β ₂for Δ Q ₂for in threshold value, β ₃for Δ Q ₂for large threshold value; Process and in the Fuzzy Inference Model that water circulation system loss volumetric flow rate causes suction well liquid level change amount, think Δ Q at slag ₂be less than β ₁time be S2, Δ Q ₂equal β ₂time be M2, Δ Q ₂be greater than β ₃time be B2;

6) utilize slag process water circulation system loss volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about Δ Q ₂fuzzy membership functions, set up following slag and process water circulation system loss volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\mathrm{\&Delta;}{h}_2=f_s\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_1\&times;e^{\frac{t}{R_{2}A}}+f_M\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_2\&times;e^{\frac{t}{R_{2}A}}+f_B\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_3\&times;e^{\frac{t}{R_{2}A}};$

7) obtain the calculation formula of suction well liquid level change total amount:

Δh＝Δh ₁+Δh ₂+Δh ₃；

Wherein, Δ h is suction well liquid level change amount total amount; Δ h ₃for granulation working shaft volumetric flow rate causes suction well liquid level change amount, calculation formula is: q ₃for granulation working shaft volumetric flow rate.

Beneficial effect of the present invention is: the complicated bad working environments that is applicable to blast furnace ironmaking; in the time that breaking down, liquidometer can provide suction well liquid level signal accurately for granulation working shaft Controlling System; ensure the smooth running of blast furnace slag processing system, improved current blast furnace ironmaking production automation level.

Brief description of the drawings

Fig. 1 is the schema of one embodiment of the invention.

Fig. 2 is the compliance test result figure of one embodiment of the invention.

Embodiment

Below in conjunction with specific examples and accompanying drawing, the present invention will be further described.

On certain blast furnace slag processing system, test based on a kind of blast furnace slag processing system suction well level measuring method of the present invention.Blast furnace ironmaking production process and equipment are various, and slag disposal system belongs to its peripheral auxiliary equipment, and blast furnace L1 and the communication of L2 two-stage are sent to blast furnace process computer system by measurement signal and carry out subsequent calculations and demonstration.In engineering application, initial liquid level is taken from the retention value in a PLC sampling period.While operation for the first time as PLC system, need the initial liquid level of field measurement and manually input at HMI interface, all the other intrinsic parameters are manually input in HMI interface according to on-site electrical equipment and suction well parameter.

Fig. 1 is the schema of one embodiment of the invention, and based on Fig. 1, the idiographic flow that the present embodiment carries out blast furnace slag processing system suction well level gauging is:

1) set up water compensating valve volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF}(P_1-P_2)\mathrm{isS}1,\mathrm{THEN\&Delta;}{h}_1=k_1\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isM}1,\mathrm{THEN\&Delta;}{h}_1=k_2\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isB}1,\mathrm{THEN\&Delta;}{h}_1=k_3\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

Wherein P ₁for the pressure before water compensating valve, P ₂for the pressure after water compensating valve, unit is Pa; S1, M1, B1 are respectively description (P ₁– P ₂) be little, in, large fuzzy number; Δ h ₁for water compensating valve volumetric flow rate causes suction well liquid level change amount, unit is m; k _jfor the gain factor under j article of fuzzy rule, be dimensionless unit, j=1,2,3, k _jinitial value by technologist by obtaining in manual operation experimental knowledge; A is orifice coefficient, and unit is s/m ^0.5, it depends on structural shape and the fluid flow of water compensating valve; A ₀take over sectional area for water compensating valve, unit is m ², its value is provided by valve producer; T is the PLC systematic sampling cycle, and unit is s; G is universal gravity constant, and unit is m/s ², desirable approximation is 9.8m/s ²; R is fluid severe, and unit is kg/m ³, its value can be consulted or be determined by experiment from relevant handbook; A is suction well sectional area, and unit is m ²;

2) in conjunction with (P ₁– P ₂) size the influencing characteristic of suction well liquid level change amount is set about (P ₁– P ₂) fuzzy membership functions:

(P ₁– P ₂) about the fuzzy membership functions f of S1 _s(P ₁– P ₂):

$f_s(P_1-P_2)=\left\{\begin{array}{cc}1,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_2\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of M1 _m(P ₁– P ₂):

$f_M(P_1-P_2)=\left\{\begin{array}{cc}0,& \text{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_3}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_2\&le;(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of B1 _b(P ₁– P ₂):

$f_B(P_1-P_2)=\left\{\begin{array}{cc}0,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3},& \mathrm{if}{\mathrm{\&alpha;}}_2<(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 1,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.;$

Here α, ₁for (P ₁– P ₂) be little threshold value, α ₂for (P ₁– P ₂) threshold value in being, α ₃for (P ₁– P ₂) be large threshold value; In Fuzzy Inference Model, think (P ₁– P ₂) be less than α ₁time be S1, (P ₁– P ₂) equal α ₂time be M1, (P ₁– P ₂) be greater than α ₃time be B1;

3) utilize water compensating valve volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about (P ₁– P ₂) fuzzy membership functions, set up following water compensating valve volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\begin{array}{c}\mathrm{\&Delta;}{h}_1=f_s(P_1-P_2)\&times;k_1\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}+f_M(P_1-P_2)\&times;k_2\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}\\ +f_B(P_1-P_2)\&times;k_3\&times;\frac{a{A}_{0}t\sqrt{\left(P_1{-P}_2\right)\frac{2g}{r}}}{A}\end{array},$

4) set up slag and process water circulation system loss volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_1\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_2\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_3=m_3\&times;e^{\frac{t}{R_{2}A}};$

Wherein Δ Q ₂for slag cycle for the treatment of system loss volumetric flow rate, unit is m ³/ s; S2, M2, B2 are respectively and describe Δ Q ₂for little, in, large fuzzy number; Δ h ₂for slag cycle for the treatment of system loss volumetric flow rate causes suction well liquid level change amount, unit is m; m _ifor the gain factor under i article of fuzzy rule, be dimensionless unit, m _iinitial value by technologist by obtaining in manual operation experimental knowledge; R ₂for the liquid resistance of flushing cinder water, unit is s/m ², by measuring;

5) in conjunction with Δ Q ₂size the influencing characteristic of suction well liquid level change amount is set about Δ Q ₂fuzzy membership functions:

Δ Q ₂fuzzy membership functions about S2:

$f_s\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}1,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_2\end{array}\right.,$

Δ Q ₂fuzzy membership functions about M2:

$f_M\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_3}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_2\&le;\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array},,\right.$

Δ Q ₂fuzzy membership functions about B2:

$f_B\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3},& \mathrm{if}{\mathrm{\&beta;}}_2<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 1,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array}\right.;$

β ₁for Δ Q ₂for little threshold value, β ₂for Δ Q ₂for in threshold value, β ₃for Δ Q ₂for large threshold value; Process and in the Fuzzy Inference Model that water circulation system loss volumetric flow rate causes suction well liquid level change amount, think Δ Q at slag ₂be less than β ₁time be S2, Δ Q ₂equal β ₂time be M2, Δ Q ₂be greater than β ₃time be B2;

6) utilize slag process water circulation system loss volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about Δ Q ₂fuzzy membership functions, set up following slag and process water circulation system loss volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\mathrm{\&Delta;}{h}_2=f_s\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_1\&times;e^{\frac{t}{R_{2}A}}+f_M\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_2\&times;e^{\frac{t}{R_{2}A}}+f_B\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_3\&times;e^{\frac{t}{R_{2}A}};$

7) obtain the calculation formula of suction well liquid level change total amount:

Δh＝Δh ₁+Δh ₂+Δh ₃；

Wherein, Δ h is suction well liquid level change amount total amount, and unit is m; Δ h ₃for granulation working shaft volumetric flow rate causes suction well liquid level change amount, unit is m, and calculation formula is: q ₃for granulation working shaft volumetric flow rate, unit is m ³/ s.

In order to verify the validity of blast furnace slag processing system suction well level measuring method of the present invention, our collection site real data and measurement model data compare, as shown in Figure 2.In Fig. 2, we have carried out the comparison of several situations: (1) manual unlocking water compensating valve is also closed granulation working shaft (t=1～2min), as can be seen from Figure 2, now liquid level go up approximately linear slope maximum, tally with the actual situation and collection in worksite data close with measurement model data; (2) manual unlocking water compensating valve open granulation working shaft (t=2～6min), as can be seen from Figure 2, in the approximately linear slope ratio that now liquid level goes up, a kind of situation is low, but slope is still positive, tally with the actual situation and collection in worksite data close with measurement model data; (3) manual-lock water compensating valve and close granulation working shaft (t=6～7min), as can be seen from Figure 2, now approximately linear slope is born, tally with the actual situation and collection in worksite data close with measurement model data.

The blast furnace slag processing system suction well level measuring method of the application of the invention; adapt to the complicated bad working environments of blast furnace ironmaking completely; in the time that breaking down, liquidometer can provide suction well liquid level signal accurately for granulation working shaft Controlling System; realize the interlocking with working shaft; ensure the smooth running of blast furnace slag processing system, improved current blast furnace ironmaking production automation level.

Above embodiment is only for calculating thought of the present invention and feature are described, its object is to make those skilled in the art can understand content of the present invention and implement according to this, and protection scope of the present invention is not limited to above-described embodiment.So the disclosed principle of all foundations, equivalent variations or the modification that mentality of designing is done, all within protection scope of the present invention.

Claims (1)

1. a blast furnace slag processing system suction well level measuring method, is characterized in that: it comprises the following steps:

1) set up water compensating valve volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF}(P_1-P_2)\mathrm{isS}1,\mathrm{THEN\&Delta;}{h}_1=k_1\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isM}1,\mathrm{THEN\&Delta;}{h}_1=k_2\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

$\mathrm{IF}(P_1-P_2)\mathrm{isB}1,\mathrm{THEN\&Delta;}{h}_1=k_3\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A};$

Wherein P ₁for the pressure before water compensating valve, P ₂for the pressure after water compensating valve; S1, M1, B1 are respectively description (P ₁– P ₂) be little, in, large fuzzy number; Δ h ₁for water compensating valve volumetric flow rate causes suction well liquid level change amount; k _jfor the gain factor under j article of fuzzy rule, be dimensionless unit, j=1,2,3, k _jinitial value by technologist by obtaining in manual operation experimental knowledge; A is orifice coefficient, and it depends on structural shape and the fluid flow of water compensating valve; A ₀for water compensating valve is taken over sectional area; T is the PLC systematic sampling cycle; G is universal gravity constant; R is fluid severe; A is suction well sectional area;

2) in conjunction with (P ₁– P ₂) size the influencing characteristic of suction well liquid level change amount is set about (P ₁– P ₂) fuzzy membership functions:

(P ₁– P ₂) about the fuzzy membership functions f of S1 _s(P ₁– P ₂):

$f_s(P_1-P_2)=\left\{\begin{array}{cc}1,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_2\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of M1 _m(P ₁– P ₂):

$f_M(P_1-P_2)=\left\{\begin{array}{cc}0,& \text{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_1\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_1}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_1}{{\mathrm{\&alpha;}}_1-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_1<(P_1-P_2)<{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_3}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_2},& \mathrm{if}{\mathrm{\&alpha;}}_2\&le;(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 0,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.,$

(P ₁– P ₂) about the fuzzy membership functions f of B1 _b(P ₁– P ₂):

$f_B(P_1-P_2)=\left\{\begin{array}{cc}0,& \mathrm{if}(P_1-P_2)\&le;{\mathrm{\&alpha;}}_2\\ \frac{1}{{\mathrm{\&alpha;}}_3-{\mathrm{\&alpha;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&alpha;}}_2}{{\mathrm{\&alpha;}}_2-{\mathrm{\&alpha;}}_3},& \mathrm{if}{\mathrm{\&alpha;}}_2<(P_1-P_2)<{\mathrm{\&alpha;}}_3\\ 1,& \mathrm{if}(P_1-P_2)\&GreaterEqual;{\mathrm{\&alpha;}}_3\end{array}\right.;$

Here α, ₁for (P ₁– P ₂) be little threshold value, α ₂for (P ₁– P ₂) threshold value in being, α ₃for (P ₁– P ₂) be large threshold value; In Fuzzy Inference Model, think (P ₁– P ₂) be less than α ₁time be S1, (P ₁– P ₂) equal α ₂time be M1, (P ₁– P ₂) be greater than α ₃time be B1;

3) utilize water compensating valve volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about (P ₁– P ₂) fuzzy membership functions, set up following water compensating valve volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\begin{array}{c}\mathrm{\&Delta;}{h}_1=f_s(P_1-P_2)\&times;k_1\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}+f_M(P_1-P_2)\&times;k_2\&times;\frac{a{A}_{0}t\sqrt{(P_1-P_2)\frac{2g}{r}}}{A}\\ +f_B(P_1-P_2)\&times;k_3\&times;\frac{a{A}_{0}t\sqrt{\left(P_1{-P}_2\right)\frac{2g}{r}}}{A}\end{array},$

4) set up slag and process water circulation system loss volumetric flow rate and cause the Fuzzy Inference Model of suction well liquid level change amount:

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_1\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_2=m_2\&times;e^{\frac{t}{R_{2}A}};$

$\mathrm{IF\&Delta;}{Q}_2\mathrm{isS}2,\mathrm{THEN\&Delta;}{h}_3=m_3\&times;e^{\frac{t}{R_{2}A}};$

Wherein Δ Q ₂for slag cycle for the treatment of system loss volumetric flow rate; S2, M2, B2 are respectively and describe Δ Q ₂for little, in, large fuzzy number; Δ h ₂for slag cycle for the treatment of system loss volumetric flow rate causes suction well liquid level change amount; m _ifor the gain factor under i article of fuzzy rule, be dimensionless unit, m _iinitial value by technologist by obtaining in manual operation experimental knowledge; R ₂for the liquid resistance of flushing cinder water, by measuring;

5) in conjunction with Δ Q ₂size the influencing characteristic of suction well liquid level change amount is set about Δ Q ₂fuzzy membership functions:

Δ Q ₂fuzzy membership functions about S2:

$f_s\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}1,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_2\end{array}\right.,$

Δ Q ₂fuzzy membership functions about M2:

$f_M\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_1\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_1}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_1}{{\mathrm{\&beta;}}_1-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_1<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3}\&times;\mathrm{\&Delta;}{Q}_2+\frac{{\mathrm{\&beta;}}_3}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2},& \mathrm{if}{\mathrm{\&beta;}}_2\&le;\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 0,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array},,\right.$

Δ Q ₂fuzzy membership functions about B2:

$f_B\left(\mathrm{\&Delta;}{Q}_2\right)=\left\{\begin{array}{cc}0,& \mathrm{if\&Delta;}{Q}_2\&le;{\mathrm{\&beta;}}_2\\ \frac{1}{{\mathrm{\&beta;}}_3-{\mathrm{\&beta;}}_2}\&times;(P_1-P_2)+\frac{{\mathrm{\&beta;}}_2}{{\mathrm{\&beta;}}_2-{\mathrm{\&beta;}}_3},& \mathrm{if}{\mathrm{\&beta;}}_2<\mathrm{\&Delta;}{Q}_2<{\mathrm{\&beta;}}_3\\ 1,& \mathrm{if\&Delta;}{Q}_2\&GreaterEqual;{\mathrm{\&beta;}}_3\end{array}\right.;$

β ₁for Δ Q ₂for little threshold value, β ₂for Δ Q ₂for in threshold value, β ₃for Δ Q ₂for large threshold value; Process and in the Fuzzy Inference Model that water circulation system loss volumetric flow rate causes suction well liquid level change amount, think Δ Q at slag ₂be less than β ₁time be S2, Δ Q ₂equal β ₂time be M2, Δ Q ₂be greater than β ₃time be B2;

6) utilize slag process water circulation system loss volumetric flow rate cause the fuzzy model of suction well liquid level change amount with about Δ Q ₂fuzzy membership functions, set up following slag and process water circulation system loss volumetric flow rate and cause the computation model of suction well liquid level change amount:

$\mathrm{\&Delta;}{h}_2=f_s\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_1\&times;e^{\frac{t}{R_{2}A}}+f_M\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_2\&times;e^{\frac{t}{R_{2}A}}+f_B\left(\mathrm{\&Delta;}{Q}_2\right)\&times;m_3\&times;e^{\frac{t}{R_{2}A}};$

7) obtain the calculation formula of suction well liquid level change total amount:

Δh＝Δh ₁+Δh ₂+Δh ₃；

Wherein, Δ h is suction well liquid level change amount total amount; Δ h ₃for granulation working shaft volumetric flow rate causes suction well liquid level change amount, calculation formula is: q ₃for granulation working shaft volumetric flow rate.